Predictive Diagnosis of Alzheimer's Disease using Machine Learning

Alzheimer's disease, characterized by memory loss and cognitive impairment, is a prevalent neurodegenerative condition. Recent studies using combined ADNI and OASIS datasets have achieved a balanced accuracy of 90.6% in early detection, though machine learning accuracy has varied. This study explores the efficacy of machine learning for early Alzheimer's detection, focusing on the Random Forest model, which attained a 90.6% balanced accuracy using the OASIS dataset alone. Our findings underscore the robustness and generalizability of machine learning models in this context. Key factors influencing classification, primarily neural characteristics, align with Alzheimer's pathology and underscore the critical role of neuroimaging biomarkers. This research highlights significant advancements in Alzheimer's diagnosis enabled by machine learning, emphasizing model robustness and the importance of appropriate dataset selection. Tailored diagnostic techniques can enhance precision, which is crucial in clinical settings.